Wear Behavior of Roller-Burnished High Interstitial Austenitic Stainless Steel Parts in Glass-Reinforced Plastic Melt

Herein this investigation, profiled high interstitial austenitic stainless steel parts are burnished on a profile-rolling machine, and afterward, the wear behavior is analyzed in a melt of glass-reinforced polypropylene. Wear and corrosion resistance are significant properties of steel parts in the plastics and food industries. The high work-hardening ability of high interstitial austenitic stainless steel enables burnishing parts with a significant local hardness to increase up to maximum values of ≈600 HV 1. Wear tests on a recently developed test stand reveal that the burnished austenitic stainless steel surface performs similarly to a nitrided surface of the standard nitriding steel 31CrMoV9 + QT with a hardness of ≈830 HV 0.5. Regarding the given advantage of corrosion resistance, it is concluded that roller burnishing supports the applicability of high interstitial austenitic stainless steel in plastics and food industries.     

Glyco-Steroidal Amphiphiles (GSAs) for Membrane Protein Structural Study

Integral membrane proteins pose considerable challenges to high resolution structural analysis. Maintaining membrane proteins in their native state during protein isolation is essential for structural study of these bio-macromolecules. Detergents are the most commonly used amphiphilic compounds for stabilizing membrane proteins in solution outside a lipid bilayer. We previously introduced a glyco-diosgenin (GDN) detergent that was shown to be highly effective at stabilizing a wide range of membrane proteins. This steroidal detergent has additionally gained attention due to its compatibility with membrane protein structure study via cryo-EM. However, synthetic inconvenience limits widespread use of GDN in membrane protein study. To improve its synthetic accessibility and to further enhance detergent efficacy for protein stabilization, we designed a new class of glyco-steroid-based detergents using three steroid units: cholestanol, cholesterol and diosgenin. These new detergents were efficiently prepared and showed marked efficacy for protein stabilization in evaluation with a few model membrane proteins including two G protein-coupled receptors. Some new agents were not only superior to a gold standard detergent, DDM (n-dodecyl-β-d -maltoside), but were also more effective than the original GDN at preserving protein integrity long term. These agents represent valuable alternatives to GDN, and are likely to facilitate structural determination of challenging membrane proteins.     

Molecularly Imprinted Polymers as Synthetic Antibodies for Protein Recognition: The Next Generation

Molecularly imprinted polymers (MIPs) are chemical antibody mimics obtained by nanomoulding the 3D shape and chemical functionalities of a desired target in a synthetic polymer. Consequently, they possess exquisite molecular recognition cavities for binding the target molecule, often with specificity and affinity similar to those of antigen-antibody interactions. Research on MIPs targeting proteins began in the mid-90s, and this review will evaluate the progress made till now, starting from their synthesis in a monolith bulk format through surface imprinting to biocompatible soluble nanogels prepared by solid-phase synthesis. MIPs in the latter format will be discussed more in detail because of their tremendous potential of replacing antibodies in the biomedical domain like in diagnostics and therapeutics, where the workforce of antibodies is concentrated. Emphasis is also put on the development of epitope imprinting, which consists of imprinting a short surface-exposed fragment of a protein, resulting in MIPs capable of selectively recognizing the whole macromolecule, amidst others in complex biological media, on cells or tissues. Thus selecting the ‘best’ peptide antigen is crucial and in this context a rational approach, inspired from that used to predict peptide immunogens for peptide antibodies, is described for its unambiguous identification.     

Molecularly Imprinted Polymer Hydrogel Nanoparticles: Synthetic Antibodies for Cancer Diagnosis and Therapy

Cancer is a leading cause of death worldwide and according to the World Health Organization (WHO) accounted for 10 million deaths in 2020. Promising theranostic (therapy and diagnostic) agents in the treatment of cancer are nanomaterials, which have come to the forefront because of their small size approaching those of protein complexes in the human body, and of their easy functionalization giving access to nanocomposite materials with diverse functions (fluorescence, magnetic, stimuli-responsiveness, etc.), and improved biocompatibility. Among them, affinity nanoparticles, often decorated with highly specific targeting ligands such as antibodies, aptamers, lectins and peptides, have enabled enhanced binding and exquisite recognition of biomarkers overexpressed in cancer cells. In this review, we describe an emerging class of targeting ligands, molecularly imprinted polymer hydrogel nanoparticles for their application in the early detection of disease, with the aim to improve diagnosis and treatment.     

Insights into Phase Evolution and Mechanical Behavior of the Eutectoid Ti–6.4(wt%)Ni Alloy Modified with Fe and Cr

Titanium alloys gain increasing importance in industry due to the expansion of advanced manufacturing technologies such as additive manufacturing. Conventional titanium alloys processed by such technologies suffer from formation of large primary grains and anisotropy of mechanical properties. Therefore, novel alloys are required. Herein, the effect of ternary alloying elements Fe and Cr on the Ti–6.4(wt%)Ni eutectoid system is investigated. Both elements act as eutectoid formers. Fe and Cr show sluggish transformation behavior, whereas Ni is an active eutectoid-forming element. Thereby, sluggish refers to slow and active to fast transformation kinetics. The focus of this work is on the combined addition of such elements studied under different heat-treatment conditions. It is shown in the results that largely varying microstructures can be generated resulting in hardness values ranging from 239 to 556 HV_0.1. Moreover, the formation of a substructure within the α phase of direct aged alloys is observed. The formation mechanism of this substructure is investigated in detail. The mechanical properties are discussed based on the microstructural characteristics. The presence of intermetallic Ti₂Ni phase increases the Young's modulus, whereas the presence of ω phase results in embrittlement. The results shed light upon the complex phase formation and decomposition behavior of titanium alloys based on Ti–6.4Ni.     

Workarounds as generative mechanisms for bottom-up process innovation—Insights from a multiple case study

Workarounds are goal-driven deviations from the standard operating procedures performed to overcome obstacles constraining day-to-day work. Despite starting as temporary fixes, they can become established across an organisation and trigger the innovation of processes and IT artefacts that can resolve misfits permanently. Although prior research has elicited antecedents and types of workarounds, it is not known how workarounds diffuse in an organisation and, thereby, innovating co-workers' activities, IT artefacts, and organisational structures. The results of our multiple two-year case study provide unique empirical insights into the diffusion of workarounds and how they can act as generative mechanisms for bottom-up process innovation.     

GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER

In this paper, we present GrowliFlower, a georeferenced, image-based unmanned aerial vehicle time-series dataset of two monitored cauliflower fields (0.39 and 0.60 ha) acquired in 2 years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14,000 individual cauliflower plants. The coordinates enable the extraction of complete and incomplete time-series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre- and post-defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations. The proposed dataset was created to facilitate the development and evaluation of various machine-learning approaches. It focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to advance automation in agriculture. Two baseline results of instance segmentation tasks at the plant and leaf level based on labeled instance segmentation data are presented. The complete GrowliFlower dataset is publicly available ( http://rs.ipb.uni-bonn.de/data/growliflower/ ).